“Do unlimited homework attempts improve student learning outcomes? Evidence across sociodemographic backgrounds” (with Gonzalo Dona), forthcoming in Journal of Education for Business.
“Parameter instabilities and monetary policy in a Small Open Economy: Evidence from an estimated model for the UK”, International Review of Economics and Finance, Volume 96, 2024.
"Authorities' Fiscal Forecasts in Latin America: Are they Optimistic?" (with Metodij Hadzi-Vaskov, Luca Ricci, and Alejandro Werner), Economia (LACEA Journal ), Volume 22, Issue 1, pages 135-152, 2023.
"What drives economic growth forecast revisions? " (with Metodij Hadzi-Vaskov, Luca Ricci, and Alejandro Werner), Review of International Economics, Volume 31, Issue 3, pages 1068-1092, 2023.
"Estimating the Bank of Mexico’s reaction function in the last three decades: A Bayesian DSGE approach with Rolling-Windows," The North American Journal of Economics and Finance, Volume 56, 2021.
"Dynamic Modeling of Electricity Consumption and Industrial Growth in Mexico" (with Belem Vasquez-Galan, and Olajide Oladipo), Journal of Energy and Development, Volume 43, pages 143-156, 2017.
Abstract
Recent literature suggests that psychological factors explain a substantial part of the fluctuations in the US business cycle. While these factors have started to be included in new empirical research, the forecast properties of these models are yet to be explored. This paper tests the forecast performance of a small-scale DSGE model with sentiment shocks. The assumption of rational expectations is relaxed, instead agents are assumed to behave in a near-rational fashion: every period they learn and update their beliefs using a constant gain learning algorithm. Sentiment shocks are captured by exploiting observed data on expectations and are defined as the deviations from the model implied expectations due to exogenous waves of pessimism or optimism. The forecast evaluation is accomplished by comparing the root mean squared prediction error of the benchmark 3-equation New Keynesian model at different horizons and under different expectation assumptions: rational expectations, learning, and learning with sentiment. The results show that the model with learning and sentiment shocks is not only able to compete with the other two alternatives, but it is generally better to forecast the output gap and the inflation rate.
"Unemployment gaps and welfare access: Revisiting the racial divergence in unemployment rates " (with Gonzalo Dona)
Abstract
This paper studies the unemployment rate racial divergence that started after 1930. We show that labor force participation can be used as a proxy to circumvent data constraints and study unemployment patterns in men between 25 and 54 years old. We use data from the US Census to argue that common hypotheses, such as the Great Migration and the Great Depression, are unlikely or only partial contenders to explain the racial unemployment gap. Instead, we show that the New Deal is a potential candidate that fits the data better. By encouraging welfare migration, the New Deal relief programs could have contributed to creating a long-term divergence in unemployment rates